LiQiiiii / Jeva

# Jeva
### System One Models from First Principles
**Qi Li · Xinchao Wang** 
National University of Singapore
Model · Project page · Evaluation · Fine-tuning
Jeva is a system one model (e.g., Jev) with native design. Give it a state and typed
questions; get candidate probabilities, a Boolean probability, or an expected
rubric score. The language encoder represents the state and each option
independently. A compact decision stack updates only the state against the
complete option memory, so decisions remain set-dependent while option
computation is reusable.
- **Permutation symmetry:** reordering candidates permutes their probabilities,
 rather than changing their semantic identities.
- **Variable candidate sets:** shared scoring does not impose a fixed label
 vocabulary. Encoding runs in bounded batches; all options reach the head.
- **Null awareness:** an explicit coverage estimate separates answer availability
 from competition between available candidates.
- **Two-level reuse:** cache complete option tuples, or reuse individual option
 vectors and projected keys/values across newly assembled sets.
## Citation
 [code] 
## Installation
Use Linux x86_64, an NVIDIA GPU, and a clean conda environment:
 [code] 
The requirements pair PyTorch 2.9.1 / CUDA 12.8 with matching accelerated kernels.
A compatible NVIDIA driver is required. The first request may compile kernels.
See runtime configuration for memory limits and the slower
reference installation in `requirements-core.txt`.
## Local inference
 [code] 
| Interface | Result |
| :-- | :-- |
| `Choice` | A candidate key and its probability distribution |
| `Choice(..., allow_null=True)` | A candidate key or `None`, with explicit null probability |
| `Noul` | Probability that the statement is true, between 0 and 1 |
| `Score` | Expected rubric level; supplied `values` define custom numerical units |
For open-set Choice, candidate probabilities plus `null_probability` sum to one.
`conditional_probabilities` reports preference conditional on selecting a candidate.
Noul is a truth probability, not the null outcome. Exact ties use the first
candidate; open-set ties place null first.
Mappings format options as `label: description`; lists preserve their text.
For exact dataset option text and order, use `model.predict(...)`.
The runnable example returns all three output types.
### Cache and reuse
 [code] 
With hybrid caching enabled, Jeva reuses option representations across changing requests and newly assembled candidate sets, while computing each decision anew.
### Offline loading
 [code] 
 [code] 
## Evaluation
Run the model on a local JSONL file:
 [code] 
The evaluation guide covers input format, metrics,
permutations, cache comparisons, and resumable runs. 
Jeva achieves 78.30% Typed Decisions accuracy, 98.66% null-detection
AUROC, and zero observed flips on 600 Choice items. Jeva handles 2,048 candidates
at 69.88% accuracy and 12.04 s median latency. Full option reuse across newly
assembled MLDR sets